hyperparameter-optimization

When users starts Serve cluster with a set of options (http options, checkpoint path) and then connects to it with a different set of options, we should either update it, or error out.

The components part of our codebase was written sometime ago, with older sklearn versions and before python typing was production ready.

In general, some of these files need to be cleaned up. Mostly typing of parameters and functions, adding documentation a bout these parameters and finally double checking with scikit learn that there aren't some new or deprecated parameters we still use.

To

Motivation

Since lightgbm v3.3.0, train/cv's arguments have been deprecated as described in optuna/optuna#3013. It would be great to replace such deprecated arguments with recommended way by lightgbm.

Description

Replace the deprecated arguments with callback-ish way. The ta

Related: #1433

In AutoGluon when datetime features are detected, they are simply converted to an int.

Instead, we could generate a variety of additional features, such as:

1. Year, Month, Day, Hour, Minute, Second
2. Is Weekend
3. Is Holiday / Near Holiday
4. Is Start of Month / End of Month
5. Day of Week / Day of Year

If you are interested in contributing this functionality, ple

This issue has been coming up when I use,
automl.predict_proba(input)

I am using the requirements.txt in venv. Shouldn't input have feature names?
This message did not used to come up and I don't know why.

Python 3.10 has been released. We should test it. If all the dependencies support it, we should add it to CI.

In issue SimonBlanke/Hyperactive#8 a problem was found, where the memory usage can increase rapidly.

The error is located in the "_init_search"-method from the "Search"-class.

On systems with ~12gb of ram an "out-of-memory"-error will occur when searching in a high dimensional (>20) search space.

If enter_data() is called with the same train_path twice in a row and the data itself hasn't changed, a new Dataset does not need to be created.

We should add a column which stores some kind of hash of the actual data. When a Dataset would be created, if the metadata and data hash are exactly the same as an existing Dataset, nothing should be added to the ModelHub database and the existing

Describe the bug
Code could be more conform to pep8 and so forth.

To Reproduce
https://app.codacy.com/gh/Neuraxio/Neuraxle/issues?&filters=W3siaWQiOiJMYW5ndWFnZSIsInZhbHVlcyI6W119LHsiaWQiOiJDYXRlZ29yeSIsInZhbHVlcyI6WyJDb2RlU3R5bGUiXX0seyJpZCI6IkxldmVsIiwidmFsdWVzIjpbXX0seyJpZCI6IlBhdHRlcm4iLCJ2YWx1ZXMiOltdfSx7ImlkIjoiQXV0aG9yIiwidmFsdWVzIjpbXX1d

Expected behavior
Less code st